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\title{Random Forest Classifier\\ \Large{Comparative Evaluation of Decision Trees Generation Algorithms}}
\author{Andrea Balboni, Marco Guerri, Luca Puddu}
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\begin{document}
\maketitle
\begin{abstract}
Random Forest is an ensemble classifier that consists of many decision trees and outputs
the class that is the mode of the classes output by individual trees. This project aims to realize multiple 
versions of a Random Forest classifier, using different algorithms for generating decision trees and finally compare
performances, both in terms of accuracy and computational complexity. 
Four different algorithms have been implemented: Extremely Randomized Random Forest for which both the data dimension and the threshold for the node splitting are chosen at random; 
Random Forest with Gini Index used to choose both the best split dimension and the best split threshold for that dimension; 
Random Forest with Information Gain to choose both the feature and the threshold value that maximize this index;
Random Forest with Fisher Linear Discriminant to minimize class overlap during the splitting process. 
\end{abstract}

\section{Related Work}
\input{relatedwork}
\section{Project Details}
\input{projdetintro}
\input{random}
\input{gini}
\input{informationgain}
\input{fisher}
\section{Evaluations}
\input{evaluationsintro}
\input{randomeval}
\input{ginievals}
\input{informationgainevals}
\input{fisherevals}
\section{conclusions}
\input{conclusions}

\bibliographystyle{IEEEtran}
\bibliography{ArticoloRF}
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